A spatial simulation model for the dispersal of the bluetongue vector Culicoides brevitarsis in Australia.

Kelso JK, Milne GJ - PLoS ONE (2014)

Bottom Line:
Data from midge trapping programmes were used to qualitatively validate the resulting simulation model.This extended model could then be used as a platform for addressing the effectiveness of spatially targeted vaccination strategies or animal movement bans as BTV spread mitigation measures, or the impact of climate change on the risk and extent of outbreaks.These questions involving incursive Culicoides spread cannot be simply addressed with non-spatial models.

Affiliation: School of Computer Science and Software Engineering, University of Western Australia, Crawley, Western Australia, Australia.

ABSTRACT

Background: The spread of Bluetongue virus (BTV) among ruminants is caused by movement of infected host animals or by movement of infected Culicoides midges, the vector of BTV. Biologically plausible models of Culicoides dispersal are necessary for predicting the spread of BTV and are important for planning control and eradication strategies.

Methods: A spatially-explicit simulation model which captures the two underlying population mechanisms, population dynamics and movement, was developed using extensive data from a trapping program for C. brevitarsis on the east coast of Australia. A realistic midge flight sub-model was developed and the annual incursion and population establishment of C. brevitarsis was simulated. Data from the literature was used to parameterise the model.

Results: The model was shown to reproduce the spread of C. brevitarsis southwards along the east Australian coastline in spring, from an endemic population to the north. Such incursions were shown to be reliant on wind-dispersal; Culicoides midge active flight on its own was not capable of achieving known rates of southern spread, nor was re-emergence of southern populations due to overwintering larvae. Data from midge trapping programmes were used to qualitatively validate the resulting simulation model.

Conclusions: The model described in this paper is intended to form the vector component of an extended model that will also include BTV transmission. A model of midge movement and population dynamics has been developed in sufficient detail such that the extended model may be used to evaluate the timing and extent of BTV outbreaks. This extended model could then be used as a platform for addressing the effectiveness of spatially targeted vaccination strategies or animal movement bans as BTV spread mitigation measures, or the impact of climate change on the risk and extent of outbreaks. These questions involving incursive Culicoides spread cannot be simply addressed with non-spatial models.

pone-0104646-g006: Temperature and simulated C. brevitarsis populations.Daily mean temperatures are shown in red with scale on right axis. Adult midge population density shown in blue with scale on left axis. Population density is given in units where the maximum sustainable immature population density has a value of 100. Three time series were extracted from the population initialisation simulation (see Section 2.4 subsection “Population initialisation”) for locations A: Byron Bay (latitude 28.64S), B: Kempsey (latitude 31.08S), and C: Nowra (latitude 34.94S).

Mentions:
Endemic populations where adults are present year-round can occur near the northern NSW state border, for example Byron Bay (latitude 28.64 south). Figure 6A shows the daily mean temperature and simulated population curves for the Byron Bay location over one year starting in January 1990. The data shown in Figures 6A, 6B and 6C were obtained by locating the simulation cell containing the site, and extracting the temperature and adult population density daily time series for that specific cell from the simulation used to establish the initial population (see Section 2.4 “Population initialisation”). The simulated population curve shows that temperature and breeding activity are at a maximum in January and February, explicitly reflecting known temperature dependent population dynamics. The midge population grows during this period and peaks at the end of February. The population then slowly declines with declining temperatures, as the rate of newly emerging midges does not keep pace with the midge mortality rate. Temperature and breeding activity reaches a minimum in August. By the end of October the population begins to increase as the larvae from the increased breeding activity begin to emerge. The relationship between temperature (red) and the adult population (blue) can be seen clearly in Figure 6A.

pone-0104646-g006: Temperature and simulated C. brevitarsis populations.Daily mean temperatures are shown in red with scale on right axis. Adult midge population density shown in blue with scale on left axis. Population density is given in units where the maximum sustainable immature population density has a value of 100. Three time series were extracted from the population initialisation simulation (see Section 2.4 subsection “Population initialisation”) for locations A: Byron Bay (latitude 28.64S), B: Kempsey (latitude 31.08S), and C: Nowra (latitude 34.94S).

Mentions:
Endemic populations where adults are present year-round can occur near the northern NSW state border, for example Byron Bay (latitude 28.64 south). Figure 6A shows the daily mean temperature and simulated population curves for the Byron Bay location over one year starting in January 1990. The data shown in Figures 6A, 6B and 6C were obtained by locating the simulation cell containing the site, and extracting the temperature and adult population density daily time series for that specific cell from the simulation used to establish the initial population (see Section 2.4 “Population initialisation”). The simulated population curve shows that temperature and breeding activity are at a maximum in January and February, explicitly reflecting known temperature dependent population dynamics. The midge population grows during this period and peaks at the end of February. The population then slowly declines with declining temperatures, as the rate of newly emerging midges does not keep pace with the midge mortality rate. Temperature and breeding activity reaches a minimum in August. By the end of October the population begins to increase as the larvae from the increased breeding activity begin to emerge. The relationship between temperature (red) and the adult population (blue) can be seen clearly in Figure 6A.

Bottom Line:
Data from midge trapping programmes were used to qualitatively validate the resulting simulation model.This extended model could then be used as a platform for addressing the effectiveness of spatially targeted vaccination strategies or animal movement bans as BTV spread mitigation measures, or the impact of climate change on the risk and extent of outbreaks.These questions involving incursive Culicoides spread cannot be simply addressed with non-spatial models.

Affiliation:
School of Computer Science and Software Engineering, University of Western Australia, Crawley, Western Australia, Australia.

ABSTRACT

Background: The spread of Bluetongue virus (BTV) among ruminants is caused by movement of infected host animals or by movement of infected Culicoides midges, the vector of BTV. Biologically plausible models of Culicoides dispersal are necessary for predicting the spread of BTV and are important for planning control and eradication strategies.

Methods: A spatially-explicit simulation model which captures the two underlying population mechanisms, population dynamics and movement, was developed using extensive data from a trapping program for C. brevitarsis on the east coast of Australia. A realistic midge flight sub-model was developed and the annual incursion and population establishment of C. brevitarsis was simulated. Data from the literature was used to parameterise the model.

Results: The model was shown to reproduce the spread of C. brevitarsis southwards along the east Australian coastline in spring, from an endemic population to the north. Such incursions were shown to be reliant on wind-dispersal; Culicoides midge active flight on its own was not capable of achieving known rates of southern spread, nor was re-emergence of southern populations due to overwintering larvae. Data from midge trapping programmes were used to qualitatively validate the resulting simulation model.

Conclusions: The model described in this paper is intended to form the vector component of an extended model that will also include BTV transmission. A model of midge movement and population dynamics has been developed in sufficient detail such that the extended model may be used to evaluate the timing and extent of BTV outbreaks. This extended model could then be used as a platform for addressing the effectiveness of spatially targeted vaccination strategies or animal movement bans as BTV spread mitigation measures, or the impact of climate change on the risk and extent of outbreaks. These questions involving incursive Culicoides spread cannot be simply addressed with non-spatial models.